Deep semantic content selection

ABSTRACT

To select the content to be presented to the user, a first latent vector is determined for a content item based on a first object associated with the content item. A second latent vector is determined for the content item based on a second object associated with the content item. A content item vector is then determined based on the first and second latent vectors. Furthermore, a user vector is determined based on interactions of the user with the first set of content objects and the second set of content objects. A score indicative of the likelihood of the user interacting with the content item is determined based on the content item vector and the user vector.

BACKGROUND

This disclosure relates generally to selecting content for beingdisplayed to users of an online system, and more specifically tomultilevel feature representation of users and content items forselecting content to be presented to the users of the online system.

Some online systems, such as a social networking system, providescontent items to users based on models that attempt to score or rank thecontent available in the online system based on a likelihood that a userwill be interested in the content item or based on a likelihood that theuser will interact with the content. Those models are generated based onfeedback signals. For instance, a user that has previously watchedseveral videos related to soccer might be interested in a video thatother soccer fans have previously watched. Such model may not beaccurate when only a limited amount of feedback is available for aspecific piece of content or for a specific user. That is, when a newcontent item is available for presentation to users, feedback for thecontent item to generate a model to predict the likelihood of a userbeing interested in the content item may not be available until a numberof users have interacted with the content item.

SUMMARY

To select the content to be presented to the user, user interactionswith different objects associated with a content item are separatelymodeled to generate a latent vector space representing user interactionswith each type of object. The latent vector space may represent userco-engagement with various objects or by different categories ofinteractions. For a given content item, it may be represented by thelatent vectors of each object (which may have different types), as wellas a vector describing the content item itself. For example, a contentitem may include a link to an external page as well as a link to aninternal page in a social networking system. The external page may beassociated with a first latent vector representing the external page andthe internal page may be represented by a second latent vector. Theexternal page latent vector may be determined based on user interactionswith a set of external pages. In this way, a page (or other contentitem) may be represented as the combination of latent vector spaces ofthe objects (here, an external page and internal page) related to thepage (or other content item). This permit a page to be represented byother objects related to the page, such that training a model to predictthe likelihood of interactions with the page may use the relatedobject's latent space and more effectively predict interactions with thepage without relying on existing user interactions with page to trainthe model as the model may be trained on other content items associatedwith objects represented in the same latent vector spaces.

In some embodiments, a first latent vector is determined for a contentitem based on a first object associated with the content item. The firstlatent vector is determined based on co-occurrence of interactions of afirst set of users with a first set of content objects having the sameobject type as the first object. A second latent vector is determinedfor the content item based on a second object associated with thecontent item. The second latent vector is determined based onco-occurrence of interactions of a second set of users with a second setof content objects having the same object type as the second object. Acontent item vector is then determined based on the first and secondlatent vectors. Furthermore, a user vector is determined based oninteractions of the user with the first set of content objects and thesecond set of content objects. A score indicative of the likelihood ofthe user interacting with the content item is determined based on thecontent item vector and the user vector.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a system environment in which an onlinesystem operates, according to one embodiment.

FIG. 2 is a block diagram of an architecture of an online system,according to one embodiment.

FIG. 3A is a diagram for determining embedding vectors for a user and acontent item, according to one embodiment.

FIG. 3B illustrates a diagram for determining a score indicative of thelikelihood of a user interacting with a content item, according to oneembodiment.

FIG. 4 is a flow diagram of a method for selecting content items to bedisplayed to a user of the social networking system, according to oneembodiment.

The figures depict various embodiments for purposes of illustrationonly. One skilled in the art will readily recognize from the followingdiscussion that alternative embodiments of the structures and methodsillustrated herein may be employed without departing from the principlesdescribed herein.

DETAILED DESCRIPTION

System Architecture

FIG. 1 is a block diagram of a system environment 100 for an onlinesystem 140, according to one embodiment. The system environment 100shown by FIG. 1 comprises one or more client devices 110, a network 120,one or more third-party systems 130, and the online system 140. Inalternative configurations, different and/or additional components maybe included in the system environment 100. For example, the onlinesystem 140 is a social networking system, a content sharing network, oranother system providing content to users.

The client devices 110 are one or more computing devices capable ofreceiving user input as well as transmitting and/or receiving data viathe network 120. In one embodiment, a client device 110 is aconventional computer system, such as a desktop or a laptop computer.Alternatively, a client device 110 may be a device having computerfunctionality, such as a personal digital assistant (PDA), a mobiletelephone, a smartphone, or another suitable device. A client device 110is configured to communicate via the network 120. In one embodiment, aclient device 110 executes an application allowing a user of the clientdevice 110 to interact with the online system 140. For example, a clientdevice 110 executes a browser application to enable interaction betweenthe client device 110 and the online system 140 via the network 120. Inanother embodiment, a client device 110 interacts with the online system140 through an application programming interface (API) running on anative operating system of the client device 110, such as IOS® orANDROID™.

The client devices 110 are configured to communicate via the network120, which may comprise any combination of local area and/or wide areanetworks, using both wired and/or wireless communication systems. In oneembodiment, the network 120 uses standard communications technologiesand/or protocols. For example, the network 120 includes communicationlinks using technologies such as Ethernet, 802.11, worldwideinteroperability for microwave access (WiMAX), 3G, 4G, code divisionmultiple access (CDMA), digital subscriber line (DSL), etc. Examples ofnetworking protocols used for communicating via the network 120 includemultiprotocol label switching (MPLS), transmission controlprotocol/Internet protocol (TCP/IP), hypertext transport protocol(HTTP), simple mail transfer protocol (SMTP), and file transfer protocol(FTP). Data exchanged over the network 120 may be represented using anysuitable format, such as hypertext markup language (HTML) or extensiblemarkup language (XML). In some embodiments, all or some of thecommunication links of the network 120 may be encrypted using anysuitable technique or techniques.

One or more third party systems 130 may be coupled to the network 120for communicating with the online system 140, which is further describedbelow in conjunction with FIG. 2 . In one embodiment, a third partysystem 130 is an application provider communicating informationdescribing applications for execution by a client device 110 orcommunicating data to client devices 110 for use by an applicationexecuting on the client device. In other embodiments, a third partysystem 130 provides content or other information for presentation via aclient device 110. A third party system 130 may also communicateinformation to the online system 140, such as advertisements, content,or information about an application provided by the third party system130.

FIG. 2 is a block diagram of an architecture of the online system 140,according to one embodiment. The online system 140 shown in FIG. 2includes a user profile store 205, a content store 210, an action logger215, an action log 220, an edge store 225, an embedding module 230, anembedding representation 235, a recommendation module 240, arecommendation model 245, and a content selection module 250, and a webserver 260. In other embodiments, the online system 140 may includeadditional, fewer, or different components for various applications.Conventional components such as network interfaces, security functions,load balancers, failover servers, management and network operationsconsoles, and the like are not shown so as to not obscure the details ofthe system architecture.

Each user of the online system 140 is associated with a user profile,which is stored in the user profile store 205. A user profile includesdeclarative information about the user that was explicitly shared by theuser and may also include profile information inferred by the onlinesystem 140. In one embodiment, a user profile includes multiple datafields, each describing one or more attributes of the correspondingonline system user. Examples of information stored in a user profileinclude biographic, demographic, and other types of descriptiveinformation, such as work experience, educational history, gender,hobbies or preferences, location and the like. A user profile may alsostore other information provided by the user, for example, images orvideos. In certain embodiments, images of users may be tagged withinformation identifying the online system users displayed in an image,with information identifying the images in which a user is tagged storedin the user profile of the user. A user profile in the user profilestore 205 may also maintain references to actions by the correspondinguser performed on content items in the content store 210 and stored inthe action log 220.

While user profiles in the user profile store 205 are frequentlyassociated with individuals, allowing individuals to interact with eachother via the online system 140, user profiles may also be stored forentities such as businesses or organizations. This allows an entity toestablish a presence on the online system 140 for connecting andexchanging content with other online system users. The entity may postinformation about itself, about its products or provide otherinformation to users of the online system 140 using a brand pageassociated with the entity's user profile. Other users of the onlinesystem 140 may connect to the brand page to receive information postedto the brand page or to receive information from the brand page. A userprofile associated with the brand page may include information about theentity itself, providing users with background or informational dataabout the entity.

The content store 210 stores objects that each represent various typesof content. Examples of content represented by an object include a pagepost, a status update, a photograph, a video, a link, a shared contentitem, a gaming application achievement, a check-in event at a localbusiness, a brand page, or any other type of content. Online systemusers may create objects stored by the content store 210, such as statusupdates, photos tagged by users to be associated with other objects inthe online system 140, events, groups or applications. In someembodiments, objects are received from third-party applications orthird-party applications separate from the online system 140. In oneembodiment, objects in the content store 210 represent single pieces ofcontent, or content “items.” Hence, online system users are encouragedto communicate with each other by posting text and content items ofvarious types of media to the online system 140 through variouscommunication channels. This increases the amount of interaction ofusers with each other and increases the frequency with which usersinteract within the online system 140.

One or more content items included in the content store 210 includecontent for presentation to a user and a bid amount. The content istext, image, audio, video, or any other suitable data presented to auser. In various embodiments, the content also specifies a page ofcontent. For example, a content item includes a landing page specifyinga network address of a page of content to which a user is directed whenthe content item is accessed. The bid amount is included in a contentitem by a user and is used to determine an expected value, such asmonetary compensation, provided by an advertiser to the online system140 if content in the content item is presented to a user, if thecontent in the content item receives a user interaction when presented,or if any suitable condition is satisfied when content in the contentitem is presented to a user. For example, the bid amount included in acontent item specifies a monetary amount that the online system 140receives from a user who provided the content item to the online system140 if content in the content item is displayed. In some embodiments,the expected value to the online system 140 of presenting the contentfrom the content item may be determined by multiplying the bid amount bya probability of the content of the content item being accessed by auser.

In various embodiments, a content item includes various componentscapable of being identified and retrieved by the online system 140.Example components of a content item include: a title, text data, imagedata, audio data, video data, a landing page, a user associated with thecontent item, or any other suitable information. The online system 140may retrieve one or more specific components of a content item forpresentation in some embodiments. For example, the online system 140 mayidentify a title and an image from a content item and provide the titleand the image for presentation rather than the content item in itsentirety.

In some embodiments, content items are associated with one or moreobjects. For instance, objects content items may be associated withinclude a webpage (for a landing page), a mobile application, and aproduct. The social networking system 140 may include an object store250 that stores information about the different objects. Furthermore,object store 250 may store information regarding the interaction ofusers of the social networking system 140 and the different objects.

Various content items may include an objective identifying aninteraction that a user associated with a content item desires otherusers to perform when presented with content included in the contentitem. Example objectives include: installing an application associatedwith a content item, indicating a preference for a content item, sharinga content item with other users, interacting with an object associatedwith a content item, or performing any other suitable interaction. Ascontent from a content item is presented to online system users, theonline system 140 logs interactions between users presented with thecontent item or with objects associated with the content item.Additionally, the online system 140 receives compensation from a userassociated with content item as online system users perform interactionswith a content item that satisfy the objective included in the contentitem.

Additionally, a content item may include one or more targeting criteriaspecified by the user who provided the content item to the online system140. Targeting criteria included in a content item request specify oneor more characteristics of users eligible to be presented with thecontent item. For example, targeting criteria are used to identify usershaving user profile information, edges, or actions satisfying at leastone of the targeting criteria. Hence, targeting criteria allow a user toidentify users having specific characteristics, simplifying subsequentdistribution of content to different users.

In one embodiment, targeting criteria may specify actions or types ofconnections between a user and another user or object of the onlinesystem 140. Targeting criteria may also specify interactions between auser and objects performed external to the online system 140, such as ona third party system 130. For example, targeting criteria identifiesusers that have taken a particular action, such as sent a message toanother user, used an application, joined a group, left a group, joinedan event, generated an event description, purchased or reviewed aproduct or service using an online marketplace, requested informationfrom a third party system 130, installed an application, or performedany other suitable action. Including actions in targeting criteriaallows users to further refine users eligible to be presented withcontent items. As another example, targeting criteria identifies usershaving a connection to another user or object or having a particulartype of connection to another user or object.

The action logger 215 receives communications about user actionsinternal to and/or external to the online system 140, populating theaction log 220 with information about user actions. Examples of actionsinclude adding a connection to another user, sending a message toanother user, uploading an image, reading a message from another user,viewing content associated with another user, and attending an eventposted by another user. In addition, a number of actions may involve anobject and one or more particular users, so these actions are associatedwith the particular users as well and stored in the action log 220.

The action log 220 may be used by the online system 140 to track useractions on the online system 140, as well as actions on third partysystems 130 that communicate information to the online system 140. Usersmay interact with various objects on the online system 140, andinformation describing these interactions is stored in the action log220. Examples of interactions with objects include: commenting on posts,sharing links, checking-in to physical locations via a client device110, accessing content items, and any other suitable interactions.Additional examples of interactions with objects on the online system140 that are included in the action log 220 include: commenting on aphoto album, communicating with a user, establishing a connection withan object, joining an event, joining a group, creating an event,authorizing an application, using an application, expressing apreference for an object (“liking” the object), and engaging in atransaction. Additionally, the action log 220 may record a user'sinteractions with advertisements on the online system 140 as well aswith other applications operating on the online system 140. In someembodiments, data from the action log 220 is used to infer interests orpreferences of a user, augmenting the interests included in the user'suser profile and allowing a more complete understanding of userpreferences.

The action log 220 may also store user actions taken on a third partysystem 130, such as an external website, and communicated to the onlinesystem 140. For example, an e-commerce website may recognize a user ofan online system 140 through a social plug-in enabling the e-commercewebsite to identify the user of the online system 140. Because users ofthe online system 140 are uniquely identifiable, e-commerce websites,such as in the preceding example, may communicate information about auser's actions outside of the online system 140 to the online system 140for association with the user. Hence, the action log 220 may recordinformation about actions users perform on a third party system 130,including webpage viewing histories, advertisements that were engaged,purchases made, and other patterns from shopping and buying.Additionally, actions a user performs via an application associated witha third party system 130 and executing on a client device 110 may becommunicated to the action logger 215 by the application for recordationand association with the user in the action log 220.

In one embodiment, the edge store 225 stores information describingconnections between users and other objects on the online system 140 asedges. Some edges may be defined by users, allowing users to specifytheir relationships with other users. For example, users may generateedges with other users that parallel the users' real-life relationships,such as friends, co-workers, partners, and so forth. Other edges aregenerated when users interact with objects in the online system 140,such as expressing interest in a page on the online system 140, sharinga link with other users of the online system 140, and commenting onposts made by other users of the online system 140.

An edge may include various features each representing characteristicsof interactions between users, interactions between users and objects,or interactions between objects. For example, features included in anedge describe a rate of interaction between two users, how recently twousers have interacted with each other, a rate or an amount ofinformation retrieved by one user about an object, or numbers and typesof comments posted by a user about an object. The features may alsorepresent information describing a particular object or user. Forexample, a feature may represent the level of interest that a user hasin a particular topic, the rate at which the user logs into the onlinesystem 140, or information describing demographic information about theuser. Each feature may be associated with a source object or user, atarget object or user, and a feature value. A feature may be specifiedas an expression based on values describing the source object or user,the target object or user, or interactions between the source object oruser and target object or user; hence, an edge may be represented as oneor more feature expressions.

The edge store 225 also stores information about edges, such as affinityscores for objects, interests, and other users. Affinity scores, or“affinities,” may be computed by the online system 140 over time toapproximate a user's interest in an object or in another user in theonline system 140 based on the actions performed by the user. A user'saffinity may be computed by the online system 140 over time toapproximate the user's interest in an object, in a topic, or in anotheruser in the online system 140 based on actions performed by the user.Computation of affinity is further described in U.S. patent applicationSer. No. 12/978,265, filed on Dec. 23, 2010, U.S. patent applicationSer. No. 13/690,254, filed on Nov. 30, 2012, U.S. patent applicationSer. No. 13/689,969, filed on Nov. 30, 2012, and U.S. patent applicationSer. No. 13/690,088, filed on Nov. 30, 2012, each of which is herebyincorporated by reference in its entirety. Multiple interactions betweena user and a specific object may be stored as a single edge in the edgestore 225, in one embodiment. Alternatively, each interaction between auser and a specific object is stored as a separate edge. In someembodiments, connections between users may be stored in the user profilestore 205, or the user profile store 205 may access the edge store 225to determine connections between users.

The embedding module 230 applies machine learning techniques to generatean embedding representation 235 that includes embedding vectors (latentvectors) that describes the entities in latent space. As used herein,latent space is a vector space where each dimension or axis of thevector space is a latent or inferred characteristic of the objects inthe space. Latent characteristics are characteristics that are notobserved, but are rather inferred through a mathematical model fromother variables that can be observed. In some embodiments, one or morelatent characteristics are observable or measurable characteristics, butthe embedding module 230 infers the value of the observablecharacteristic from other characteristics of the entity instead ofdirectly measuring the observable characteristic.

The embedding representation 235 includes embedding vectors for users(user vectors). The embedding representation 235 is trained based on theco-occurrence of user interactions in the social networking system(e.g., co-occurrence of user interactions with pages in the socialnetworking system, applications in the social networking system, orposts in the social networking system). The embedding representation 235may be further trained based on user interactions in third-partysystems. For example, the embedding module 230 may use a purchasinghistory of the user in a third-party online retailer to train theembedding representation 235. The embedding representation 235 may befurther trained based on a webpage browsing history of the user. In someembodiments, the embedding representation 235 is trained so that usersthat have interacted with the same content items are located closer toeach other in latent space. After the embedding representation 235 istrained for determining embedding vectors for users, an embedding vectorfor any user may be determined using information of the user availableby the online system.

The embedding representation 235 includes embedding vectors for contentitems of the social networking system. The embedding representation 235is trained based on different features of the content item. Forinstance, the embedding module 230 may use the words contained in thecontent item, an image or video contained in the content item, a landingpage of the content item, a product associated with the content item,and a user associated with the content item to train the embeddingrepresentation 235. In some embodiments, the embedding representation235 includes sub-vectors for each of the features of the content item,and the sub-vectors are concatenated to generate the embedding vector ofthe content item.

In some embodiments, the embedding representation 235 includes anengagement embedding representation 235A, a web embedding representation235B, an app embedding representation 235C, and a word embeddingrepresentation 235D. The engagement embedding representation 235A istrained based on user/page organic co-engagements. The web embeddingrepresentation 235B is trained based on webpage organic visits made byusers. The app embedding representation 235C is trained based on mobileapp organic installs made by users. The word embedding representation235 is trained based on available text documents (e.g., usingWikipedia's documents, or text included in content items). In someembodiments, some of the embedding representations 235 are trained usinginformation stored by the social networking system 140 about objectsstored in the object store. In other embodiments, some of the embeddingrepresentations 235 are trained using information stored in the actionlog 220.

FIG. 3A illustrates a diagram for determining embedding vectors for auser and a content item, according to one embodiment. An embeddingsub-vector {user_(page):emb_vec} is identified from the engagementembedding representation 235A trained based on user/page organicengagement data, embedding sub-vector {userweb:emb_vec} is identifiedfrom the web embedding representation 235B trained based on User/Weborganic visits data, embedding sub-vector {user_(app):emb_vec} isidentified from the app embedding representation 235C trained based onUser/App organic installs data, and embedding sub-vector{user_(word):emb_vec} is identified from the word embeddingrepresentation 235D trained based on words included in available textdocuments. Furthermore, embedding sub-vector {content_(page):emb_vec}for the page associated with the content item, embedding sub-vector{content_(web):emb_vec} for a landing page (webpage) associated with thecontent item, embedding sub-vector {content_(app):emb_vec} for a mobileapplication associated with the content item, and embedding sub-vector{content_(word):emb_vec} based on the words in the body of the contentitem are identified from corresponding embedding representations 235.The embedding module 230 then concatenates the sub-vectors{user_(page):emb_vec}, {user_(page):emb_vec}, {user_(app):emb_vec}, and{user_(word):emb_vec} to obtain the embedding vector for the user, andconcatenates the sub-vectors {content_(page):emb_vec},{content_(page):emb_vec}, {content_(app):emb_vec}, and{content_(word):emb_vec} to obtain the embedding vector for the contentitem.

In some embodiments, sub-vectors are only identified for the contentitems, and a single embedding vector is identified for the user. Theembedding module 230 obtains the embedding vector for the user directlyfrom the embedding representation 235, and obtains the embedding vectorfor the content item by concatenating the sub-vectors.

Referring back to FIG. 2 , the recommendation module 240 determines ascore indicative of a likelihood of a user interaction with the contentitem based on the embedding vector of the content item and a user vectorof a user. As used herein, a user interaction with a content item occurswhen a user takes a specific action with a content item presented to theuser. The actions to be taken by the user may, for example, be clickinga link included in the content item impression, playing a video includedin the content item, installing an app associated with the content item,or purchasing a product associated with the content item. Since theembedding vectors for users and the embedding vectors for the contentitems might be in different latent spaces, directly comparison betweenthe embedding vectors might not be a reliable indication of thelikelihood of users interacting with the content items. Instead, anembedding vector for a user and an embedding vector for a content itemare used as inputs to a recommendation model 245. The recommendationmodel 245 then determines a score indicative of the likelihood of theuser interacting with the content item. In some embodiments, therecommendation model 245 maps the embedding vectors of users and theembedding vectors of the content items to a new latent space, so thatdirect comparison is possible. In this embodiment, after both theembedding vector for a user and the embedding vector for the contentitem have been translated to the new latent space, a distance or anglebetween the embedding vectors may be calculated as the likelihood of theuser interacting with the content item.

The recommendation module 240 trains a recommendation model 245 based onhistorical content item impressions to users of the social networkingsystem. That is, historical user interactions with content items areused as positive training samples to train the recommendation model 245.The recommendation module 240 may further use historical content itemimpressions that did not result in a user interaction as negativetraining samples to train the recommendation model 245.

FIG. 3B illustrates a diagram for determining a score indicative of thelikelihood of a user interacting with a content item, according to oneembodiment. A user vector and a content vector are identified using theembedding representation 235. In the example of FIG. 3B, the user vectoris generated by concatenating user sub-vectors {user_(page):emb_vec},{user_(page): emb_vec}, {user_(app):emb_vec}, and {user_(word):emb_vec}from the embedding representation 235, and the content vector isgenerated by concatenating content sub-vectors {content_(page):emb_vec}, {content_(page):emb_vec}, {content_(app):emb_vec}, and{content_(word):emb_vec} from the embedding representation 235. The uservector and the content vector are then provided to the recommendationmodel 245. The recommendation model 245 determines a score indicative ofthe likelihood of the user interacting with the content item based onthe user vector and the content vector.

Referring back to FIG. 2 , the content selection module 250 selects oneor more content items for communication to a client device 110 to bepresented to a user. Content items eligible for presentation to the userare retrieved from the content store 210 or from another source by thecontent selection module 250, which selects one or more of the contentitems for presentation to the viewing user. A content item eligible forpresentation to the user is a content item associated with at least athreshold number of targeting criteria satisfied by characteristics ofthe user or is a content item that is not associated with targetingcriteria. In various embodiments, the content selection module 250includes content items eligible for presentation to the user in one ormore selection processes, which identify a set of content items forpresentation to the user. For example, the content selection module 250determines measures of relevance of various content items to the userbased on characteristics associated with the user by the online system140 and based on the user's affinity for different content items. Basedon the measures of relevance, the content selection module 250 selectscontent items for presentation to the user. As an additional example,the content selection module 250 selects content items having thehighest measures of relevance or having at least a threshold measure ofrelevance for presentation to the user. Alternatively, the contentselection module 250 ranks content items based on their associatedmeasures of relevance and selects content items having the highestpositions in the ranking or having at least a threshold position in theranking for presentation to the user.

Content items eligible for presentation to the user may include contentitems associated with bid amounts. The content selection module 250 usesthe bid amounts associated with ad requests when selecting content forpresentation to the user. In various embodiments, the content selectionmodule 250 determines an expected value associated with various contentitems based on their bid amounts and selects content items associatedwith a maximum expected value or associated with at least a thresholdexpected value for presentation. An expected value associated with acontent item represents an expected amount of compensation to the onlinesystem 140 for presenting the content item. For example, the expectedvalue associated with a content item is a product of the bid amount anda likelihood of the user interacting with the content item as determinedby the recommendation module 240. The content selection module 250 mayrank content items based on their associated bid amounts and selectcontent items having at least a threshold position in the ranking forpresentation to the user. In some embodiments, the content selectionmodule 250 ranks both content items not associated with bid amounts andcontent items associated with bid amounts in a unified ranking based onbid amounts and measures of relevance associated with content items.Based on the unified ranking, the content selection module 250 selectscontent for presentation to the user. Selecting content items associatedwith bid amounts and content items not associated with bid amountsthrough a unified ranking is further described in U.S. patentapplication Ser. No. 13/545,266, filed on Jul. 10, 2012, which is herebyincorporated by reference in its entirety.

For example, the content selection module 250 receives a request topresent a feed of content to a user of the online system 140. The feedmay include one or more content items associated with bid amounts andother content items, such as stories describing actions associated withother online system users connected to the user, which are notassociated with bid amounts. The content selection module 250 accessesone or more of the user profile store 205, the content store 210, theaction log 220, and the edge store 225 to retrieve information about theuser. For example, information describing actions associated with otherusers connected to the user or other data associated with usersconnected to the user are retrieved. Content items from the contentstore 210 are retrieved and analyzed by the content selection module 250to identify candidate content items eligible for presentation to theuser. For example, content items associated with users who are notconnected to the user or stories associated with users for whom the userhas less than a threshold affinity are discarded as candidate contentitems. Based on various criteria, the content selection module 250selects one or more of the content items identified as candidate contentitems for presentation to the identified user. The selected contentitems are included in a feed of content that is presented to the user.For example, the feed of content includes at least a threshold number ofcontent items describing actions associated with users connected to theuser via the online system 140.

In various embodiments, the content selection module 250 presentscontent to a user through a newsfeed including a plurality of contentitems selected for presentation to the user. One or more content itemsmay also be included in the feed. The content selection module 250 mayalso determine the order in which selected content items are presentedvia the feed. For example, the content selection module 230 orderscontent items in the feed based on likelihoods of the user interactingwith various content items as determined by the recommendation module240.

The web server 260 links the online system 140 via the network 120 tothe one or more client devices 110, as well as to the one or more thirdparty systems 130. The web server 260 serves web pages, as well as othercontent, such as JAVA®, FLASH®, XML and so forth. The web server 260 mayreceive and route messages between the online system 140 and the clientdevice 110, for example, instant messages, queued messages (e.g.,email), text messages, short message service (SMS) messages, or messagessent using any other suitable messaging technique. A user may send arequest to the web server 260 to upload information (e.g., images orvideos) that are stored in the content store 210. Additionally, the webserver 260 may provide application programming interface (API)functionality to send data directly to native client device operatingsystems, such as IOS®, ANDROID™, or BlackberryOS.

Multi-Level Feature Representation for Content Item Selection

FIG. 4 is a flow diagram of a method for selecting content items to bedisplayed to a user of the social networking system, according to oneembodiment. The embedding module 230 determines 405 an embedding vectorfor a user of the social networking user. The embedding module 230further determines 410 an embedding vector for a content item that iseligible to be presented to the user. The recommendation module 240determines 415 a score indicative of the likelihood of the userinteracting with the content item based on the determined user embeddingvector and content item embedding vector. In some embodiments, todetermine the score, the recommendation module maps the embedding vectorfor the user and the embedding vector for the content item to a commonlatent space, and determines a measure of similarity between the mappedembedding vectors.

If there are other content items identified by the content selectionmodule 250, embedding vectors are determined for each of the identifiedcontent items and a score indicative of the likelihood of the userinteracting with each of the identified content items is determined.After all the content items identified by the content selection module250 are analyzed, the content selection module 250 ranks 430 theidentified content items based on the determined scores and selects 435the top ranked content items to be provided to the user for display.

CONCLUSION

The foregoing description of the embodiments has been presented for thepurpose of illustration; it is not intended to be exhaustive or to limitthe patent rights to the precise forms disclosed. Persons skilled in therelevant art can appreciate that many modifications and variations arepossible in light of the above disclosure.

Some portions of this description describe the embodiments in terms ofalgorithms and symbolic representations of operations on information.These algorithmic descriptions and representations are commonly used bythose skilled in the data processing arts to convey the substance oftheir work effectively to others skilled in the art. These operations,while described functionally, computationally, or logically, areunderstood to be implemented by computer programs or equivalentelectrical circuits, microcode, or the like. Furthermore, it has alsoproven convenient at times, to refer to these arrangements of operationsas modules, without loss of generality. The described operations andtheir associated modules may be embodied in software, firmware,hardware, or any combinations thereof.

Any of the steps, operations, or processes described herein may beperformed or implemented with one or more hardware or software modules,alone or in combination with other devices. In one embodiment, asoftware module is implemented with a computer program productcomprising a computer-readable medium containing computer program code,which can be executed by a computer processor for performing any or allof the steps, operations, or processes described.

Embodiments may also relate to an apparatus for performing theoperations herein. This apparatus may be specially constructed for therequired purposes, and/or it may comprise a general-purpose computingdevice selectively activated or reconfigured by a computer programstored in the computer. Such a computer program may be stored in anon-transitory, tangible computer readable storage medium, or any typeof media suitable for storing electronic instructions, which may becoupled to a computer system bus. Furthermore, any computing systemsreferred to in the specification may include a single processor or maybe architectures employing multiple processor designs for increasedcomputing capability.

Embodiments may also relate to a product that is produced by a computingprocess described herein. Such a product may comprise informationresulting from a computing process, where the information is stored on anon-transitory, tangible computer readable storage medium and mayinclude any embodiment of a computer program product or other datacombination described herein.

Finally, the language used in the specification has been principallyselected for readability and instructional purposes, and it may not havebeen selected to delineate or circumscribe the patent rights. It istherefore intended that the scope of the patent rights be limited not bythis detailed description, but rather by any claims that issue on anapplication based hereon. Accordingly, the disclosure of the embodimentsis intended to be illustrative, but not limiting, of the scope of thepatent rights, which is set forth in the following claims.

What is claimed is:
 1. A method comprising: determining a first userlatent vector for a user and a first object latent vector for a contentitem based on a first object associated with the content item, the firstobject having a first object type, wherein the first user latent vectorand the first object latent vector are in a first latent space anddetermined using a first model trained based on co-occurrence ofinteractions of a first set of users with a first set of content objectshaving the first object type; determining a second user latent vectorfor the user and a second object latent vector for the content itembased on a second object associated with the content item, the secondobject having a second object type, wherein the second user latentvector and the second object latent vector are in a second latent spaceand determined using a second model trained based on co-occurrence ofinteractions of a second set of users with a second set of contentobjects having the second object type, the second model different thanthe first model; determining a third user latent vector for the user anda third object latent vector for the content item based on a thirdobject associated with the content item, the third object having a thirdobject type, wherein the third user latent vector and the third objectlatent vector are in a third latent space and determined using a thirdmodel trained based on co-occurrence of interactions of a third set ofusers with a third set of content objects having the third object type,the third model different than the first model and the second model;determining a content item latent vector based on the first objectlatent vector, the second object latent vector, and the third objectlatent vector; determining a user latent vector based on the first userlatent vector, the second user latent vector, and the third user latentvector; and determining, using a machine-learned model trained based onpast interactions of a plurality of users and a plurality of contentitems, a score indicative of a likelihood of the user interacting withthe content item, the score based on the content item latent vector andthe user latent vector.
 2. The method of claim 1, wherein determiningthe score indicative of the likelihood of the user interacting with thecontent item comprises: mapping the user latent vector to a fourthlatent space using the machine-learned model; mapping the content itemlatent vector for the content item to the fourth latent space using themachine-learned model; and determining a measure of similarity betweenthe mapped user latent vector in the fourth latent space and the mappedcontent item latent vector for the content item in the fourth latentspace.
 3. The method of claim 2, wherein the fourth latent space is acombination of the first latent space, the second latent space, and thethird latent space.
 4. The method of claim 1, further comprising:determining scores for a plurality of content items, each scoreindicative of a likelihood of the user interacting with a respectivecontent item of the plurality of content items; and ranking theplurality of content items based on the determined scores of theplurality of content items.
 5. The method of claim 1, furthercomprising: identifying a plurality of content items that are eligibleto be presented to the user based on characteristics of the user.
 6. Themethod of claim 1, wherein the machine-learned model for determining thescore indicative of the likelihood of the user interacting with thecontent item is trained based on the user's past interactions withcontent items.
 7. The method of claim 1, wherein the first objectassociated with the content item is one of a product associated with thecontent item, a domain associated with the content item, an accountassociated with the content item, an image or video associated with thecontent item, an application associated with the content item, a pageassociated with the content item, and words associated with the contentitem.
 8. The method of claim 7, wherein the second object associatedwith the content item is one of the product associated with the contentitem, the domain associated with the content item, the accountassociated with the content item, the image or video associated with thecontent item, the application associated with the content item, the pageassociated with the content item, and the words associated with thecontent item different from the first object.
 9. The method of claim 1,wherein the user latent vector is determined based on the user'sengagement in a social networking system.
 10. The method of claim 1,wherein the user latent vector is determined based on the user'sactivity in one or more third party systems.
 11. The method of claim 1,wherein determining the content item latent vector comprisesconcatenating the first object latent vector and the second objectlatent vector, and wherein determining the user latent vector comprisesconcatenating the first user latent vector and the second user latentvector.
 12. The method of claim 1, further comprising: receiving arequest for a feed of content items to be presented to the user; andselecting the content item for presentation in the feed based on thedetermined score indicative of the likelihood of the user interactingwith the content item; and causing a client device associated with theuser to present the feed to the user.
 13. A non-transitory computerreadable storage medium storing instructions, the instructions whenexecuted by a processor cause the processor to: determine a first userlatent vector for a user and a first object latent vector for a contentitem based on a first object associated with the content item, the firstobject having a first object type, wherein the first user latent vectorand the first object latent vector are in a first latent space anddetermined using a first model trained based on co-occurrence ofinteractions of a first set of users with a first set of content objectshaving the first object type; determine a second user latent vector anda second object latent vector for the content item based on a secondobject associated with the content item, the second object having asecond object type, wherein the second user latent vector and the secondobject latent vector are in a second latent space and determined using asecond model trained based on co-occurrence of interactions of a secondset of users with a second set of content objects having the secondobject type, the second model different than the first model; determinea third user latent vector for the user and a third object latent vectorfor the content item based on a third object associated with the contentitem, the third object having a third object type, wherein the thirduser latent vector and the third object latent vector are in a thirdlatent space and determined using a third model trained based onco-occurrence of interactions of a third set of users with a third setof content objects having the third object type, the third modeldifferent than the first model and the second model; determine a contentitem latent vector based on the first object latent vector, the secondobject latent vector, and the third object latent vector; determine auser latent vector based on the first user latent vector, the seconduser latent vector, and the third user latent vector; and determine,using a machine-learned model trained based on past interactions of aplurality of users and a plurality of content items, a score indicativeof a likelihood of the user interacting with the content item, the scorebased on the content item latent vector and the user latent vector. 14.The non-transitory computer readable storage medium of claim 13, whereindetermining the score indicative of the likelihood of the userinteracting with the content item comprises: mapping the user latentvector to a fourth latent space using the machine-learned model; mappingthe content item latent vector for the content item to the fourth latentspace using the machine-learned model; and determining a measure ofsimilarity between the mapped user latent vector in the fourth latentspace and the mapped content item latent vector for the content item inthe fourth latent space.
 15. The non-transitory computer readablestorage medium of claim 13, wherein the instructions further cause theprocessor to: determine scores for a plurality of content items, eachscore indicative of a likelihood of the user interacting with arespective content item of the plurality of content items; and rank theplurality of content items based on the determined scores of theplurality of content items.
 16. The non-transitory computer readablestorage medium of claim 13, wherein the machine-learned model fordetermining the score indicative of the likelihood of the userinteracting with the content item is trained based on the user's pastinteractions with content items.
 17. The non-transitory computerreadable storage medium of claim 13, wherein the first object is one ofa product associated with the content item, a domain associated with thecontent item, an account associated with the content item, an image orvideo associated with the content item, an application associated withthe content item, a page associated with the content item, and wordsassociated with the content item.
 18. The non-transitory computerreadable storage medium of claim 13, wherein the user latent vector isdetermined based on at least one of: (1) the user's engagement in asocial networking system and (2) the user's activity in one or morethird party systems.
 19. The non-transitory computer readable storagemedium of claim 13, wherein determining the content item latent vectorcomprises concatenating the first object latent vector and the secondobject latent vector, and wherein determining the user latent vectorcomprises concatenating the first user latent vector and the second userlatent vector.
 20. A system comprising: one or more processors; and anon-transitory computer readable storage medium storing instructions,the instructions when executed by a processor cause the processor to:determine a first user latent vector for a user and a first objectlatent vector for a content item based on a first object associated withthe content item, the first object having a first object type, whereinthe first user latent vector and the first object latent vector are in afirst latent space and determined using a first model trained based onco-occurrence of interactions of a first set of users with a first setof content objects having the first object type; determine a second userlatent vector and a second object latent vector for the content itembased on a second object associated with the content item, the secondobject having a second object type, wherein the second user latentvector and the second object latent vector are in a second latent spaceand determined using a second model trained based on co-occurrence ofinteractions of a second set of users with a second set of contentobjects having the second object type, the second model different thanthe first model; determine a third user latent vector for the user and athird object latent vector for the content item based on a third objectassociated with the content item, the third object having a third objecttype, wherein the third user latent vector and the third object latentvector are in a third latent space and determined using a third modeltrained based on co-occurrence of interactions of a third set of userswith a third set of content objects having the third object type, thethird model different than the first model and the second model;determine a content item latent vector based on the first object latentvector, the second object latent vector, and the third object latentvector; determine a user latent vector based on the first user latentvector, the second user latent vector, and the third user latent vector;and determine, using a machine-learned model trained based on pastinteractions of a plurality of users and a plurality of content items, ascore indicative of a likelihood of the user interacting with thecontent item, the score based on the content item latent vector and theuser latent vector.